a reinforcement learning based tag recommendation

ISCAS OpenIR

	a reinforcement learning based tag recommendation
	Ge Feng; He Yi; Liu Jin; Lv Xiaoming; Zhang Wensheng; Li Yiqun
	2011
会议录名称	Advances in Intelligent and Soft Computing
页码	251-258
收录类别	EI
ISSN	1867-5662
ISBN	9783642256578
部门归属	(1) State Key Laboratory of Software Engineering Computer School Wuhan University 430072 China; (2) Logistics Group Wuhan University 430072 China; (3) State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China; (4) Baidu Inc. 100085 China
摘要	This paper proposes a reinforcement learning based tag recommendation algorithm to deal with the data sparseness that affects the performance stability of collaborative filtering algorithms. Our algorithm integrates user tags into traditional collaborative filtering algorithms and attaching importance to user interest shift in the process of user interest learning process. Empirical Cases of comparing with traditional collaborative filtering algorithms indicate that our recommend algorithm exhibits better performance competition. © 2011 Springer-Verlag Berlin Heidelberg.; This paper proposes a reinforcement learning based tag recommendation algorithm to deal with the data sparseness that affects the performance stability of collaborative filtering algorithms. Our algorithm integrates user tags into traditional collaborative filtering algorithms and attaching importance to user interest shift in the process of user interest learning process. Empirical Cases of comparing with traditional collaborative filtering algorithms indicate that our recommend algorithm exhibits better performance competition. © 2011 Springer-Verlag Berlin Heidelberg.
关键词	Intelligent Systems Knowledge Engineering Reinforcement Reinforcement Learning Signal Filtering And Prediction
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16295
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Ge Feng,He Yi,Liu Jin,et al. a reinforcement learning based tag recommendation[C],2011:251-258.